[TYPES/announce] MSR PhD Scholarship, deadline Nov 1: Machine Learning and Natural Language Processing for Types in "Big Code"

Andy Gordon (RESEARCH) adg at microsoft.com
Fri Oct 14 07:12:26 EDT 2016

> ====================================================================


> PhD studentship: Microsoft Research PhD Scholarship:

>    Machine Learning and Natural Language Processing for Types in "Big Code"


> Supervisor:

>    Charles Sutton, University of Edinburgh


> Apply by 1 November for full consideration.

>    Later applications will still be considered if position unfilled.


> More information:


>* http://www.ed.ac.uk/informatics/postgraduate/fees/research-scholarships/research-grant-funding/ms-research-phd-scholarship-machine-learning-nlp

> * http://homepages.inf.ed.ac.uk/csutton/

> * Email Charles Sutton <csutton at inf.ed.ac.uk<mailto:csutton at inf.ed.ac.uk>>


> ====================================================================


> Project Description


> The goal of this PhD studentship is to develop new machine learning

> methods to predict facts about computer programs by combining

> information from the source code with dynamic information from

> runtime. One of the most common such facts are types of variables,

> methods, etc; this information is so useful in preventing bugs that

> programming languages like C# and Java require that programs contain

> explicit annotations for the types of all program entities, even if

> the type can be deterministically inferred from other information in

> the program.


> But any annotation to a program comes with a cost to add the types to

> a program and maintain them. Indeed, it is the desire to reduce this

> cost has led to the popularity of non-statically typed languages such

> as Python and JavaScript. More advanced research in programming

> languages have developed rich languages for specifying more detailed

> facts about programs, such as refinement types that allow for logical

> constraints on variable values. These richer types can identify more

> subtle bugs at compile time, but they come with a correspondingly

> greater cost to add and maintain.


> This research project aims to obtain the benefits of rich type

> annotations at lower cost, by developing machine learning methods to

> automatically predict rich type annotations of programs that do not

> contain explicit type annotations. We will develop new methods drawing

> from probabilistic graphical models and deep learning to combine

> information from the names in a program, from dynamic analysis, and

> from the types of deterministic constraints used in traditional static

> analysis.


> This studentship is an opportunity to combine cutting edge research in

> machine learning, statistical natural language processing, and

> programming languages. The project will be supervised by Dr Charles

> Sutton at the University of Edinburgh, in collaboration with Dr Earl

> Barr at UCL and Prof Andrew D Gordon of Microsoft Research.


> During the course of their PhD, the Scholar will be invited to

> Microsoft Research in Cambridge for an annual PhD Summer School with

> talks and poster sessions, which provides an opportunity to present

> work to Microsoft researchers and Cambridge academics.


> What's required?


> The project is suitable for a student with a top MSc or first-class

> bachelor's degree in computer science, statistics, physics, or a

> related numerate discipline. Previous coursework or experience in

> machine learning, statistical natural language processing, and

> programming languages is desirable, although we do not expect students

> to have all three of these. Because of the scale of the data set

> involved, a strong programming background will be essential for this

> project.


> Our research group


> The School of Informatics at the University of Edinburgh has one of

> the largest concentrations of computer science research in Europe,

> with over 100 faculty members and 275 PhD students. The school is

> particularly strong in the three research areas most relevant to this

> project, machine learning, natural language processing, and

> programming languages. Our strength in these areas have been

> recognized by awards of EPSRC Centres for Doctoral Training in

> Pervasive Parallelism and in Data Science - this project cuts across

> the remit of these two centres. The University of Edinburgh is one of

> the founding partners of the Alan Turing Institute, the UK's national

> research institute for data science. For more information on the

> research in Dr Sutton's group, see:

> http://homepages.inf.ed.ac.uk/csutton/


> Initial enquiries


> For informal enquiries about the studentship, please contact Dr

> Charles Sutton. Formal application must be through the School's normal

> PhD application process:

> http://www.ed.ac.uk/schools-departments/informatics/postgraduate/apply

> Select the Informatics: Institute for Adaptive and Neural Computation

> research area.


> Application deadline


> For full consideration, please apply by 1 November 2016. We will aim

> to fill the studentship as soon as possible, so that the successful

> applicant can begin in the spring semester 2017.


> Funding Notes


> The Microsoft scholarship consists of an annual bursary up to a

> maximum of three years.

> This is a fully funded studentship for UK and EU students. For

> overseas applicants, we can provide funding for stipend and for fees

> only to the UK/EU level. The remaining fees component will need to

> come from another source. Overseas applicants are advised to apply

> before the standard Informatics deadlines and apply for other

> scholarships.
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